Biostatistics and Computational Biology (BCB) Core

CORE 2: BIOSTATISTICS AND COMPUTATIONAL BIOLOGY: SUMMARY/ABSTRACT The Biostatistics and Computational Biology (BCB) Core for this P01 will enable cost-effective data processing, analysis, and contextualization of the omics data generated for all Projects. This will span a wide range of methods in omics, such as short-read NGS, CAPP-seq, long-read genome Sequencing (PDx, patient, and

mouse models), Cut & Run, Hi-C (mini-C, Capture-C), ChIP-Seq/Mint-ChIP, scATAC seq/ scRNAseq / scRRBS / sc-multiome, spatial-omics methods (OME-TIFF, DICOM), metabolomics and spatial metabolomics, as well as support for other genomic assays (Methylated RNA-seq, scPCR, sc rtPCR) and integrative analysis of the above

omics data sets. This will span a suite of integrated algorithms on our computational ICB infrastructure, including r -make (RNA-seq), methylKit, eDMR, GATK, methClone, mCaller/Megalodon (nanopore data), and Nextflow workflows for SnapATAC and Seurat (v4.0), with data processing logs and QC at every step of each pipeline.

We will also coordinate data processing and sharing with the two other Cores: the mouse model / patient organoid Core (Core 1) and the imaging and pathology core (Core 3). Our Core includes faculty and staff from both the Meyer Cancer Center at WCM and the Department of Biostatistics at MD Anderson, both with extensive

collaborative experience with each other and project leaders of the P01. For this P01, we have built frameworks for terabytes of data and built agile interfaces for the interpretation of these data across five aims: (Aim 1) generate primary sequence data, run quality control (QC) assessment of sequence data, perform alignment, and analyze the genomics and transcriptomic data generated in the projects,

(Aim 2) produce epigenomic data (RRBS, WGBS, Cut&Run, scATAC/RNA-seq, and hmC-profiling) and guide analysis for clonality inference and interpretation using our open-source algorithms, (Aim 3) perform integrative characterization of samples, organize and back-up data for public release, and provide a centralized server

environment for P01, (Aim 4) provide statistical designs, including experimental configurations, sample sizes, and power calculations, for all research projects, (Aim 5) provide data analysis, generate statistical reports for all projects, and assist project investigators in publishing scientific results.

Across all aims, we will help guide integrative analysis of the data, coordinate data release (SRA, dbGAP), aid with manuscript and statistical interpretation, and create a Lymphoma Epigenomics Portal for the P01 and data broader data access.